1. Technical Field
This application relates to data generation, and more particularly to interpolation techniques used in data generation.
2. Description of Related Art
A physical or engineering system may be modeled based on experimental data. The experimental data may be limited in a variety of different ways since it may be difficult to obtain such data. For example, the experimental data may represent an incomplete or limited set of conditions and measurements. The experimental data may also be characterized as scattered and non-uniform. Interpolation techniques may be used to generate additional data based on the experimental data. However, irregularly scattered experimental data may limit the utilization of some interpolation techniques.
The problem complexity may increase as the number of dimensions of the data set increases. Existing interpolation techniques, such as Delaunay tesselation, may not be scalable for use with larger data dimensions. As the dimensionality of the data increases, the costs associated with the computation and storage may become prohibitive.
Some interpolation techniques, such as the nearest neighbor, may be more readily scaleable for use with larger data dimensions due to their speed and more modest memory requirements. However, these techniques may have other drawbacks, such as introducing larger interpolation errors and sensitivity to scaling of data sets. For example, using the nearest neighbor interpolation technique with different scaling factors for the same data point may result in different nearest neighbors being determined for the same data point.
It may be desirable, therefore, to have an interpolation technique that may use as input non-uniform and scattered data to efficiently generate additional data for a desired set of conditions. It may also be desirable to have an interpolation technique that is scalable for use with larger data dimensions while reducing the storage and computational costs as compared with existing techniques.